aleatory

Aleatory (/ˈeɪliətəri/) is a Python library for simulating and visualising stochastic processes. It introduces objects representing stochastic processes, and provides functionality to:

• generate realizations/trajectories of each process over discrete time sets

• create visualisations to illustrate the processes properties and behaviour

Currently, aleatory supports the following stochastic processes:

• Arithmetic Brownian Motion (see Brownian Motion)

• Bessel process

• Brownian Bridge

• Brownian Excursion

• Brownian Meander

• Brownian Motion

• Constant Elasticity Variance (CEV) process

• Cox–Ingersoll–Ross (CIR) process

• Chan-Karolyi-Longstaff-Sanders (CKLS) process

• Fractional Brownian Motion process

• Galton-Watson process with Poisson branching

• Gamma process

• Gaussian process with Constant kernel

• Gaussian process with Linear kernel

• Gaussian process with Matern kernel

• Gaussian process with Periodic kernel

• Gaussian process with RBF kernel

• Gaussian process with Squared Exponential kernel

• General Random Walk

• Geometric Brownian Motion

• Hawkes process

• Inverse Gaussian process

• Inhomogeneous Poisson process

• Mixed Poisson process

• Ornstein–Uhlenbeck (OU) process

• Poisson process

• Random Walk

• Squared Bessel processes

• Vasicek process

• Variance-Gamma process

• White Noise kernel

Installation

Aleatory is available on pypi and can be installed as follows

pip install aleatory

Installation from GitHub

It is possible to install the latest version of the package by cloning its GitHub repository and doing the manual installation as follows

git clone https://github.com/quantgirluk/aleatory.git
pip install ./aleatory

Dependencies

Aleatory relies heavily on

• numpy and scipy for random number generation, as well as support for a number of one-dimensional distributions, and special functions.

• matplotlib for creating visualisations

Compatibility

Aleatory is tested on Python versions 3.8, 3.9, 3.10, and 3.11.

Quick-Start Guide

• Quick-Start Guide
  • The simulate() method
  • The plot() method
  • The draw() method
  • Charts Customisation

Modules

• Stochastic Processes
• Probability Distributions

Indices and tables

• Index

• Search Page